Snowflake vs BigQuery: A Look Inside The Warehouse Wars

What is a data warehouse and how do you pick the right provider? See below for more details and a look inside the Snowflake vs BigQuery battle.

Snowflake vs BigQuery: A Look Inside The Warehouse Wars
Photo by Ruchindra Gunasekara / Unsplash

Everyone knows data is a critical fuel for making more informed business decisions — but how do you store it and structure it optimally?

The answer lies in a data warehouse. This system is used for data analysis and reporting, acting as a core component of business intelligence and the modern data stack. With the ability to pull data from several different sources, a data warehouse is a centralized storage location.

So, what is a data warehouse, and how do you pick the right provider? See below for more details and a look inside the Snowflake vs BigQuery battle.

What Is a Data Warehouse?

A data warehouse is a data management system designed to support business intelligence, particularly analytics.

The concept of a data warehouse is new for many companies, but this ability to store data is decades old. The architecture for data warehouses was developed in the 1980s to transform data from operational to decision-making support systems. However, until recently, organizations had to implement significant on-premise infrastructure.

As big data became more and more crucial to companies and cloud computing technology improved, data warehouses evolved into what they are today. Amazon Redshift was one of the first to launch back in 2012 and continues to be a front runner.

The core benefits of implementing a data warehouse include:

  • The ability to make more informed decisions
  • Consolidating data from several sources
  • Access to historical data
  • Greater consistency and accuracy concerning data quality

So now that you’ve got a basic understanding

Data warehouses are now built for the modern age, offering the ability to take in data from multiple sources, including third-party apps and services, customer support systems, and internal databases. The key difference between a database and a warehouse is that a database is relational while a warehouse is columnar. The result is that queries are almost always faster in a warehouse.

When comparing available data warehouses, here are the top options.

Snowflake

Snowflake is one of the most popular and easy-to-use data warehouses available, with flexibility being a top-selling point. Since this platform is cloud-agnostic, it can be deployed anywhere, including Google Cloud, AWS, and Azure. Snowflake also offers a pay-as-you-go pricing model. This makes it a great option for organizations that want to scale their usage up or down as needed. Once you pull your data to Snowflake via an ELT tool, you can start using it immediately.

Google BigQuery

Many teams compare Snowflake vs BigQuery. Like Google's other products, BigQuery is straightforward to use, cloud-based, and free (up to 10 GB). Unlike Snowflake, BigQuery is a managed service that’s included in the Google Cloud Platform. This means that users will have to pay for the entire Google Cloud Platform to use BigQuery. In addition to its integration with the rest of Google's services, BigQuery is one of the most significant advantages of BigQuery. You can access insights, intelligence features, and predictions.

Amazon Redshift

As discussed, Amazon Redshift helped establish the data warehousing industry. Redshift is a fast, simple, cost-effective data warehousing service. It enables you to run complex queries on billions of rows of data in just a few seconds. Redshift is fully managed, meaning that you don't have to worry about provisioning, patching, or managing your data warehouse. Redshift is also highly scalable, so you can easily add more nodes to your data warehouse as your needs grow. Redshift is easy to use, so you can get up and running quickly. And Redshift integrates seamlessly with other AWS services, so you can use it to build powerful data-driven applications.

Azure Synapse Analytics

Previously known as Microsoft Azure SQL Data Warehouse, Azure Synapse Analytics is a cloud-based warehouse that's an optimal choice for those already using the Microsoft suite of business tools. Azure can deliver insights from all your data at fast speeds, offering a ton of scalability. Security is one area where this data warehouse shines, thanks to Dynamic Data Masking (DDM), which masks sensitive data to non-privileged users.

IBM Db2 Warehouse

IBM's Db2 Warehouse is known for its high availability and reliability. This data warehouse is built for high-performance AI and analytics. It's ideal if you want to integrate your warehouse with IBM tools and Oracle products. This option is suited for large enterprises as it offers limited usability features and has a high price point.

Firebolt

Firebolt is a popular choice among data analysts and data engineers, as its primary focus is speed. This data warehouse can handle semi-structured data and data lake scale volumes built for modern usage. As your data challenges and needs grow, Firebolt will scale with you.

Wait, so what about Databricks?

You might have come across a few more terms like Data Lakes, Data Marts, and Lake Houses that probably confused you even further.

While a data warehouse is designed explicitly for data analytics to understand trends and relationships across data better, a data lake is a centralized repository that stores data in its native, raw format. Data lakes include structured, semi-structured, and unstructured data, whereas a data warehouse is more about structured, relational data stored in files or folders.

Data lakes were developed because of the limitations of data warehouses. Data warehouses offer scalable, high-performance analytics, but they are expensive. They do not often handle modern use cases — think data science and machine learning as these require massive amounts of raw data for training.

However, data lakes also have limitations, which is why Databricks is so beneficial.

Databricks is an organization and big data platform founded by Apache Spark that combines data warehouses and data lakes into what's known as a lakehouse architecture. The goal here is to streamline your data so that you can derive new insights from a complete data source. Ultimately, a lakehouse allows traditional analytics to co-exist with machine learning and data science.

A modern frontend for your new data warehouse

Whichever data warehouse you choose, you’re going to need a front-end that makes your hard work on the backend shine. Canvas integrates with most popular data warehouses and gives your business teams a spreadsheet-like interface to access and explore data without having to bug your data or engineering teams for help. Connecting your warehouse takes less than five minutes and its free to get started. Get started now!